Establishing Regional Power Sustainability and Feasibility Using Wind Farm Land-Use Optimization

Abstract

Wind-farm planning optimization is important for decision-making concerning regional energy planning in developing countries. This process is governed by restrictions on site selection based on land suitability metric variables, wind turbine technology variables, and land-use governing criteria. This study aims to create a framework for land appropriation strategies for locating optimum sites suitable for wind farms. It is using Jordan as an Area of Interest (AOI), where the scope is to illustrate how this framework will employ wind turbine energy to positively enhance the national Gross Domestic Product (GDP). The methodology employs thirteen GIS thematic layers with a 250-m spatial resolution to substantiate how site-specific criteria, turbine type, and turbine hub height variables are determining factors in the optimal solution. This method involves selecting relevant factors, database construction, data layer generation and preparation, numerical ranking and weighting of each factor, and computation of the potential wind farm locations map by overlaying all the thematic GIS layers. The results showed that the establishment of wind farms would not only meet the AOI’s growing energy needs, rather exceed them to generating income for the developing nation. The results of the feasibility study will boost the national GDP by 3.4%; where, for example, one governorate alone could produce 274.3% of the total required national consumption at a turbine hub height of 50 m. The study attests to a valuable framework that can be implemented elsewhere to establish regional power sustainability and feasibility for other nations. The results show that an added land-use layer indicating the potential value of land in terms of its suitability for establishing wind farms should be considered in future sustainable regional planning studies when considering networks for smart cities, industrial cities, smart agriculture, and new agglomerations.

Standardizing the factors used in wind farm site suitability models: A review

“…The implicitly geospatial nature of non-physical siting factors [ 99 ] means that their continuous variation in space requires being placed onto a gridded dataset (see Mann et al‘s [ 212 ] approach to census demographics, or Brewer et al‘s [ 223 ] to social attitude surveys), or being proxied with a physical siting factor, such as inferring noise pollution or visual impact based on distance from infrastructure [ 130 , 145 , 152 , 157 , 181 , 210 ]. Either of these approaches allow non-physical siting factors to be treated alongside the datasets that commonly represent physical siting factors in GIS-based WiFSS models, such as line shapefiles of powerlines for transmission line proximity [ 74 , 80 , 136 , 144 , 164 , 179 ] or rasters of wind speed for assessing the resource itself [ 78 , 79 , 90 , 126 , 130 , 201 ]. The common function of the datasets for each siting factor is to inform a GIS-based model's assessment of wind farm potential across a continuous spatial domain.…”

“…A third facet of inconsistent implementation is the exclusion of important constraints. Compared to factors listed in Table 3 , few studies in this review enlisted constraints for Distance to Mines or Pits [ 131 , 144 , 155 ], or Distance to Fault Lines [ 150 , 165 , 168 ], despite the known risks of building wind farms in earthquake-prone areas [ 225 , 226 ] and over mines [ 227 ]. The decision to implement specific constraints in WiFSS models is sometimes context-dependent (e.g., there is no need to include fault line proximity if the study area does not experience earthquakes), but constraints having a consistent magnitude, units, and nature (prohibition or buffer distance) across studies is nevertheless important.…”

“…Table 1 a and 1b shows that most studies in this review utilized secondary datasets, particularly those with GIS-based MCDA approaches, usually in the form of downloaded geospatial data [ 81 , 90 , 124 , 145 , 181 ] and previously recorded observations [ 80 , 125 , 147 , 162 , 206 ]. Some secondary datasets were enlisted by multiple studies, such as road information obtained from OpenStreetMap [ 130 , 136 , 144 ], Digital Elevation Models from the United States Geological Survey [ 71 , 80 , 133 ], and wind speed information from the Global Wind Atlas [ 79 , 143 , 148 ]. The use of such datasets for WiFSS studies exemplifies the value of free resource access for public sector model development [ 228 ], because developers are thereby encouraged to use a common set of siting factors, facilitating standard language and comparisons of modeling approaches that are less biased by siting factor choices.…”

“…Acronyms for the listed MCDA methods: Analytic Hierarchy Process (AHP), Analytic Network Process (ANP), Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS), Multicriteria Optimization and Compromise Solution (VIKOR, in Bosnian), Ordered Weighted Averaging (OWA), Best-Worst Method (BWM), ELimination Et Choice Translating REality (ELECTRE, in French), Preference Ranking Organization Method of Enrichment Evaluation (PROMETHEE), Multicriteria Interactive Decision Making (TODIM, in Portuguese). MCDA Approach Study Context Data Type (Secondary/Primary) Weighting/Ranking Method References GIS-based Onshore Secondary AHP [ [46] , [74] , [75] , [78] , [79] , [80] , [82] , [83] , [94] , [104] , [122] , [123] , [124] , [125] , [126] , [127] , [128] , [129] , [130] , [131] , [132] , [133] , [134] , [135] , [136] , [137] , [138] , [139] , [140] , [141] , [142] , [143] , [144] , [145] , [146] , [147] , [148] ] ANP [ [149] , [150] , [151] ] …”

Contested renewable energy sites due to landscape and socio-ecological barriers: Comparison of wind and solar power installation cases in Japan

“…conflict with local communities). 2,11,12,[21][22][23] By doing so, we aim to (1) demonstrate the importance of the landscape factors, among others, having the potential to become an instrumental starting factor in the selection of wind and solar PV power project sites and (2) highlight the importance of incorporating landscape conservation measures into the legal and social systems.…”